WO2012045773A9 - Procédé de caractérisation d'un échantillon par analyse de données - Google Patents

Procédé de caractérisation d'un échantillon par analyse de données Download PDF

Info

Publication number
WO2012045773A9
WO2012045773A9 PCT/EP2011/067383 EP2011067383W WO2012045773A9 WO 2012045773 A9 WO2012045773 A9 WO 2012045773A9 EP 2011067383 W EP2011067383 W EP 2011067383W WO 2012045773 A9 WO2012045773 A9 WO 2012045773A9
Authority
WO
WIPO (PCT)
Prior art keywords
signature
sample
values
analysis result
characterizing
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/EP2011/067383
Other languages
English (en)
Other versions
WO2012045773A1 (fr
Inventor
Fritz Huber
Renate KIRCHHÖFER
Volker Pfahlert
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
LipoFIT Analytic GmbH
Original Assignee
LipoFIT Analytic GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by LipoFIT Analytic GmbH filed Critical LipoFIT Analytic GmbH
Priority to AU2011311596A priority Critical patent/AU2011311596B2/en
Priority to EP18190951.6A priority patent/EP3438651B1/fr
Priority to CA2813863A priority patent/CA2813863A1/fr
Priority to US13/878,094 priority patent/US10497465B2/en
Priority to EP11778815.8A priority patent/EP2625511B1/fr
Publication of WO2012045773A1 publication Critical patent/WO2012045773A1/fr
Publication of WO2012045773A9 publication Critical patent/WO2012045773A9/fr
Anticipated expiration legal-status Critical
Priority to US16/654,828 priority patent/US20200051669A1/en
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B99/00—Subject matter not provided for in other groups of this subclass
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N24/00—Investigating or analyzing materials by the use of nuclear magnetic resonance, electron paramagnetic resonance or other spin effects
    • G01N24/08—Investigating or analyzing materials by the use of nuclear magnetic resonance, electron paramagnetic resonance or other spin effects by using nuclear magnetic resonance
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/46—NMR spectroscopy
    • G01R33/4625—Processing of acquired signals, e.g. elimination of phase errors, baseline fitting, chemometric analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/10—Signal processing, e.g. from mass spectrometry [MS] or from PCR
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

Definitions

  • the present invention relates to a method for characterizing a sample according to the preamble of claim 1 , a method for characterizing a system according to the preamble of claim 10, the use of a substance as marker according to the preambles of claims 16 and 17 and a computer program product according to the preamble of claim 18.
  • NMR spectroscopy nuclear magnetic resonance spectroscopy
  • Statistical NMR methods are also known in which no previous signal assignment is carried out.
  • a so-called signature is generated by a method according to the invention, the signature being able to characterize the sample on the basis of a previously obtained measuring result, wherein it is not looked on single values or signals of the measuring result, but always on the value of a mathematic relation between at least two signals or values of an according analysis result.
  • these signals or values are such signals which are caused by one or several substances which are originally assigned to the analysed sample (thus, originally being contained in the sample) and are not added to the sample perhaps for normalisation purposes prior to carrying out the analysis.
  • the plurality of values, out of which the values are chosen for determining the value of the mathematic relation originate from exactly one analysis result (i.e. always e.g. 2 values of a single analysis result are set into relation to each other). It is possible to combine values of the mathematic relation originating from different sets of values (i.e. from different analysis results) for generating a single signature.
  • the signature is not only based on single lines or bands of a spectrum, but on substances causing those lines and or bands.
  • "based on substances” is to be understood as based on a parameter that is proportional to the concentration of the respective substance. E.g. a set of lines and their corresponding integrals could be such a parameter on which a signature might be based.
  • a mathematic editing of the analysis result is carried out in order to be able to describe it with a sum of single functions.
  • a continuous portfolio of values is generated so that, in contrast to prior art, no recourse to discrete values of the analysis result is necessary.
  • a mathematic iterative decomposition of the obtained analysis result into sets of lines can be carried out which can reproduce the original analysis result.
  • a suited method is a spectral deconvolution.
  • a superordinated set of lines can then be generated from all analysis results in order to pool into clusters and to filter the information contained in the different analysis results.
  • the sample which is analysed and characterized comprises a body fluid of an individual, a culture medium, seeds, a plant extract or food.
  • the sample represents one of the pre-mentioned substances.
  • a suited body fluid are blood, urine, bile, tissue fluid, sperm, lymph, saliva or cerebrospinal fluid.
  • Examples for a culture medium are media usually used for growing bacteria which media can also be used in a fermenter.
  • Examples for seeds are single or a plurality of seed grains out of which new plants can originate.
  • Examples for a plant extract are extracts, generated with a suited extractant, of parts of a living or dead plant, for example extracts obtained from roots, leaves, fruits, the cortex or a comparable plant part.
  • extracts obtained from seeds are also to be understood as plant extracts within the meaning of the present application.
  • Examples for food are sausage, meat and milk products, fruit juices or fruit juice concentrates, wine, beer, other alcoholic and non-alcoholic beverages as well as convenience products.
  • the analysis method is chosen from the group consisting of NMR spectroscopy, mass spectrometry, electron spin resonance, vibrational spectroscopy, UV/VIS spectroscopy, fluorescence spectroscopy and X-ray spectroscopy.
  • a suited example for vibrational spectroscopy is infrared spectroscopy, in particular using infrared radiation from the medium infrared range. If any of the pre-mentioned analysis methods is used in the characterizing method, the analysis result is a spectrum.
  • NMR spectroscopy is particularly well suited as analysis method. It is possible to work over a very broad measurement range by NMR spectroscopy, namely over more than six orders of magnitude (concentration of the substance in the analysis sample approximately 1 ⁇ / ⁇ to 1 mol/l). Further, hundreds of substances and substance concentrations are detected in parallel in only a single measurement. Thereby, the accuracy is better than 1 % over the whole measurement range. It is particularly favourable that also unexpected ingredients of the sample are detected so that it is not necessary to know already prior to the analysis which substances one wants to detect. In case of a human urine sample, it can be drawn upon more than 1500 single signals by an NMR spectroscopic analysis, which signals in each case represent single, pure, quantifiable conditions.
  • the sample is analysed in a variant of the method by at least two analysis methods.
  • the analysed sample can be exactly the same sample in each case or a different aliquot of the same sample in each case.
  • the sample or its composition is not compromised by the first analysis method, one comes by both courses of action to the same result.
  • the sample is indeed analysed by the same biophysical measuring method (that means exactly one analysis method is used), but the parameters in this analysis method are varied.
  • a sample (or an aliquot of a sample) can for example be subjected to a first NMR spectroscopic analysis with a first set of measuring parameters, in order to be subsequently subjected to a second NMR spectroscopic analysis with a second set of measuring parameters.
  • a single analysis method different information contents out of one and the same sample (or different aliquots of the same sample).
  • detailed information on the size distribution of lipoproteins in the serum (a so-called lipoprotein profile) can thus be obtained, in particular by NMR spectroscopy.
  • analyses with respect to unsaturated fatty acids and further lipid components as well as to phospholipids are possible.
  • the analysis method can be adjusted by different measuring parameters to a more sensitive detection of big or small molecules in each case.
  • a plurality of values of the mathematic relation between two values of the analysis result in each case are determined in a variant of the method. This means, two values of the analysis results are put in mathematic relation to each other in each case. The result is a value of this mathematic relation. The more pairs of two values of the analysis result are used, the more values of the according mathematic relation are obtained. Additional information remaining otherwise unconsidered can be obtained from the analysis result by such an increased correlation of values of the analysis result.
  • each value of the analysis result is correlated with each other value of the analysis result to obtain in this manner a plurality of values of a mathematic relation between two values in each case.
  • a 2x2 matrix, a 3x3 matrix, a 4x4 matrix, a 5x5 matrix etc. can be generated from the values of the mathematic relation.
  • the size of the matrix is dependent on the number of the considered values of the analysis result.
  • a threshold value can be given that determines whether a signal of the analysis result, to be detected in numbers, is also considered as value. Such a threshold value can thus control of how many values to be considered the analysis result consists.
  • the mathematic relation is the ratio of the respective values to each other.
  • the value of the mathematic relation is in this case the value of the quotient.
  • a characterizing signature can be generated on the basis of several values of such ratios - but also on the basis of several values of other mathematic relations - which signature subsequently enables, for example, in comparison to at least one single further already existing signature a classification of the individual, from whom the sample originates, into a certain group, like for example "normal” or "pathological".
  • a comparison with collectives exhibiting a pronounced response to a certain problem an assignment of a sample to one of those collectives on the basis of the characterizing signature can be carried out.
  • the characterizing signature is based on a mathematic relation of values of an analysis result (like NMR signal intensities or integrals).
  • the characterizing signature can be depicted as a matrix. Preferably, most values of the matrix have the value zero.
  • a distinct signature differs from another distinct signature on the one hand by the pattern of values differing from zero and on the other hand by the specific quantitative (numeric) value of those values differing from zero.
  • “Characterizing” is to be understood in particular as the determination of the health status of an individual, the determination of the risk of an organ rejection after a transplantation, the determination of the organ function after an organ transplantation or organ damaging, the efficiency of an installation, a quality control, a purity test, a determination of fitness within the framework of a development of an active agent, an optimization for culture purposes, a clarification of origin or a quality assurance.
  • Suited organs for the pre-mentioned characterisation possibilities are for example liver, kidney, spleen, lung and heart, in particular the kidney.
  • the health status of an individual can be, e.g., the development of a disease, in particular if the individual is subjected to a therapy.
  • the efficacy of the therapy can be monitored.
  • the disease is a tumour-associated disease like prostate carcinoma, cancer of the colon, lung cancer, kidney cancer, pancreas cancer, liver cancer and stomach cancer.
  • a characterizing signature can also be used for an automatic total quantification of a spectrum, as far as a known or predetermined amount of a standard is present in the examined sample.
  • a signal of defined height is caused by the predetermined amount of the standard (or another substance), the signal corresponding to a certain amount or number of protons. If now another known substance is present in the sample, also its amount can be determined since the number of protons for a molecule of this substance is known if the substance as such and its chemical structure are known. Consequently, the known substances contained in the sample can be quantified by co-measuring a standard.
  • the method exhibits in a variant a further step in which the classification of the system into a predetermined category on the basis of the deviation between the signature and the comparative signature takes place.
  • a classification can for example be effected by a support vector machine, wherein preferably no digital algorithm but an analogue algorithm is used to be able to detect intermediate conditions between single categories.
  • At least one further parameter of the system underlying the comparative signature is used for classifying the system.
  • This further parameter can also be denominated as external parameter.
  • Examples for such a further parameter are for example the properties "healthy” or “pathological” or - in particular regarding a biogas plant as system to be analyzed - "efficient plant” or "inefficient plant”.
  • the characterizing system is a biological system or an industrial system representing a biological process or a substance resulting from a biological system.
  • a biological system are a multi-cellular animal, in particular a mammal like a human or an non-human mammal, further single cells or mono-cellular creatures like bacteria, further viruses, fungi and plants.
  • an industrial system representing a biological process are a waste water treatment plant, a biogas plant, a technical fermentation process and a biotechnological plant.
  • the substance resulting from a biological system are seed, food of natural origin like fruit juice, milk products as well as meet and sausage goods or convenience products.
  • different samples of a system are, in a variant of the method, analyzed individually or in combination to each other.
  • different body fluids of an individual or also differently conditioned samples of the same body fluid of an individual can serve as different samples of a system.
  • different body fluids can be processed or analyzed in several different manners in each case in order to thus represent different samples of a system.
  • provision is made in this variant that, for example, a blood sample and a urine sample of an individual are examined by the same analysis method or by different analysis methods. Thereby, the blood sample and the urine sample can be taken from the individual for example at the same time or with a small temporal distance in order to essentially represent the same condition of the individual.
  • a supplementary parameter of the system is additionally used in a variant of the method in order to generate the signature of the sample.
  • a supplementary parameter can for example be a physiological parameter of the individual from whom the sample originates. Examples of such a physiological parameter are the blood pressure of the examined individual or its body mass index.
  • this is an external parameter.
  • Such an external parameter is a variable which was not determined by the technique underlying the analysis method or another comparable technique, but characterizes the system or individual independent on the sample.
  • the substances used as marker either individually or in any arbitrary combination are methyl malonate, lactate, methyl succinate, p-cresol, 3-hydroxy isovalerate, citrate, methyl guanidine, malonate, taurine, methyl guanidine, phenyl acetyl glycine (2-(N-phenyl acetyl) amino acetic acid), trigonelline, a-glucose, acetyl carnitine, phenyl acetate and hippurate.
  • a subject-matter of the invention is also the use of certain other substances for monitoring the course of a therapy against a tumour.
  • the substances to be used as such marker either individually or in any arbitrary combination are citrate, creatinine, malonate, methyl malonate, methyl guanidine, dimethyl succinate, hydroxy isovalerate, tartrate, salicylate, hypoxanthine, hydroxy butyrate, allothreonine, 1 -methyl urate, trimethylamine-n-oxide, glycolate, 5-hydroxy methyluracil, 3-hexene dione acid, xanthine, formiate, fatty acid (derivate), histamine, dimethyl aminopurine and benzoate.
  • exogenic substances i.e. substances externally applied to an examined individual, e.g. due to a medication
  • exogenic substances i.e. substances externally applied to an examined individual, e.g. due to a medication
  • benzoate, salicylate and tartrate are also well suited as marker since they can be metabolized in the body of an individual in a different way or not at all in dependence of the development of the disease or the therapy, respectively.
  • the tumour is chosen from the group consisting of prostate carcinoma, cancer of the colon, kidney cancer.
  • Subject-matter of the invention is also the use of the described signature technique for monitoring the progression of a disease, in particular if the individual suffering from the disease is subjected to a therapy against the disease.
  • a subject-matter of the invention is further a computer program product having a computer program which has a program code for carrying out any of the above-explained methods, if the computer program is executed on a computer.
  • the mathematic relation the value of which serves as basis for generating the characterizing signature, is chosen in such a way that concentration differences of single substances compensate each other, thus it is possible to work independent on concentration, such computer program product enables waving an additional concentration determination of the substances in the sample to be analyzed.
  • the mathematic relation is a division so that the ratio between two values of the analysis result is established.
  • the effort on a laboratory scale for characterizing the sample or the system is significantly reduced, whereby on the one hand time and on the other hand costs can be saved.
  • the data amount for the further analysis is reduced by generating the value of the mathematic relation between the at least two values of the analysis result so that subsequent method steps can be effected with a significantly lower calculation effort than this is the case without an according calculation of the value of a mathematic relation.
  • Another subject-matter is a software for carrying out any of the above-explained methods.
  • a software enables the automated execution of the explained methods. It can be used to analyze previously obtained measuring data or to also interact with a measuring device like a spectrometer for assisting the measurement of data.
  • the disclosed methods are not only suited to establish positive conclusions (like "This signature indicates that the sample belongs to group A.”), but also negative conclusions (like "This signature indicates that the sample does not belong to group A, B, C, D.”). Whereas positive conclusions are can be specifically drawn in 1 -to-1 problems, negative conclusions are helpful in 1 -to-n problems (n being a number higher than 1 ).
  • FIG. 1 shows a schematic depiction of an exemplary sampling of a biological system
  • Fig. 2 shows a schematic depiction of possible variations in sample processing
  • Fig. 3 shows a schematic depiction of a possible data acquisition from a biological system
  • Fig. 4A shows a first overview depiction for generating a characterizing signature
  • Fig. 4B shows a second overview depiction for generating a characterizing signature
  • Fig. 5 shows an NMR spectrum of a human urine sample, shows a graphic depiction of normalized sums of NMR signals of urine samples of different persons, shows a schematic depiction of a metabolic network of substances playing a role in kidney rejection assessment, shows a schematic depiction of the classification of a measured signature by using already existing signatures, shows a schematic depiction of the classification of different measured signatures by using already existing signatures, shows a further schematic depiction of the classification of different measured signatures by using already existing signatures, shows a schematic comparison of clinical diagnostic findings and a prognosis for a clinical picture generated by NMR signatures, shows NMR spectra of caffeine-containing coffee and decaffeinated coffee, shows a measured NMR spectrum of coffee and a synthetic NMR spectrum of coffee, Fig. 14 shows a graphic correlation of different coffee types to their caffeine content,
  • Fig. 15 schematically depicts a method of information coarsening
  • Fig. 16 schematically depicts a separation of strongly roasted coffees from weakly roasted coffees based on a learned separation rule
  • Fig. 17 shows a difference spectrum between the average spectrum of strongly roasted coffees and the average spectrum of weakly roasted coffees
  • Fig. 18 shows an NMR spectrum of mustard seed extract
  • Fig. 19 shows an excerpt of an original NMR spectrum of mustard seed extract together with its synthetic spectrum
  • Fig. 20 shows a schematic depiction of a metabolic network for substances playing a role in tumour metabolism
  • Fig. 21 is a schematic depiction of a computer system
  • Fig. 22 is a schematic flow-chart of an embodiment of a system for characterizing a sample
  • Fig. 23 is a schematic flow-chart of an embodiment of a system for characterizing a system.
  • Figure 1 shows a schematic depiction of sampling a biological system 100.
  • the aim of sampling is to characterize the biological system 100 and to evaluate it in a stationary manner. Thereby, the characterization can be targeted to the actual state of the biological system 100 with respect to a given question.
  • the question can for example be whether seeds originate from a variety A or from a variety B.
  • a further question could be for example whether the biological system 100 works faultlessly at the time point of taking out the sample (this is in particular of interest with respect to a biogas plant).
  • the question can also be targeted to the future state of the biological system 100.
  • the characterization of the biological system 100 can for example be targeted to determine whether the rejection of a transplanted organ is probable or not.
  • a further possible question is whether a patient will come down with diabetes or a cardiovascular disease with a higher or lower probability.
  • Yet another possible question is whether an intended drug administration or drug dosage will be efficient in a patient or not.
  • a first sample 10, a second sample 20 and a third sample 30 are taken from the biological system 100.
  • the first sample 10 can be for example urine
  • the second sample 20 for example blood plasma
  • the third sample 30 another body fluid.
  • external measuring results 40 like for example the blood pressure or the body mass index of a patient can be taken from the biological system 100.
  • the external measuring results 40 are thus measuring results which have been obtained by other methods than those methods which are used for characterizing the first sample 10, the second sample 20 and the third sample 30.
  • the sampling of the biological system 100 represents the first level.
  • Samples consisting of blood plasma or serum can for example be measured without further processing, thus in the form in which they have been taken from the body. Thereby, it is for example also possible to determine the size distribution of the lipoproteins contained in the blood plasma or the serum by NMR spectroscopy.
  • the lipoproteins being present in the according samples can also be cleaved by the addition of detergents. In doing so, the possibility exists to measure the compositions of the lipids being present in the blood by NMR spectroscopy.
  • Such a possible variation of the sample processing is depicted in Figure 2.
  • Figure 2 it is exemplarily indicated how the second sample 20 is transferred one time without further treatment (upper arrow) to the measurement of the lipoproteins contained in the second sample 20.
  • a detergent 50 can be added to the second sample 20, this resulting then in a treated second sample 25.
  • the treated second sample 25 can then be used for measuring the lipids contained therein.
  • a variation in carrying out the technologies can further be effected.
  • blood plasma samples and serum samples can be examined with different NMR pulse programs.
  • gradient edited spectra permit that information on proteins and lipoproteins (thus high molecular compounds) are measured, whereas metabolites having a lower molecular weight are suppressed.
  • NMR CPMG spectra (CPMG denotes Carr Purcell Meiboom Gill; NMR CPMG is a NMR relaxation diffusion measuring method) indicate only low molecular components (regularly low molecular metabolites), whereas high molecular compounds are suppressed. Depending on the problem, a variation in carrying out the technique adjusted to the individual needs can here thus be effected.
  • the data acquisition carried out within the framework of characterizing the biological system 100 is schematically depicted in Figure 3.
  • the first sample 10 the second sample 20 and the third sample 30 are taken from the biological system 100 within the framework of sampling. Further, external measuring results 40 are taken out (confer in this respect also Figure 1 ).
  • the second level II a variation of sample processing takes place.
  • an untreated first sample 10 a first sample 15 treated by a first method and a first sample 16 treated by a second method can thus be obtained by different sample processing techniques.
  • an untreated second sample 20 can be obtained without further sample processing.
  • an untreated third sample 30 as well as a third sample 35 treated by an according sample processing can arise.
  • each of the sample or partial sample obtained in the second level II can be subjected to different measuring methods like NMR spectroscopy and mass spectroscopy (MS).
  • Figure 4A an overview over the first step for generating a characterizing signature is depicted.
  • the first sample 10 is measured by a first method 51 , resulting in a set of first values.
  • This set of first values is depicted by the numbers 1 , 2, 3 in a semicircle-like border strip.
  • a treated second sample 25 is measured by a second method 52, resulting in a second set of values.
  • This second set of values is depicted by the numbers 1 , 2 in a diamond standing on the apex.
  • the third sample 30 is measured by a third method 53 resulting in a third set of values.
  • This third set of values is depicted by the numbers 1 , 2 in an ellipse.
  • the fourth value set being depicted by the number 1 in a diamond lying on the side can be extracted out of the external measuring results 40.
  • Figure 4B gives now an overview over the second step of signature generating.
  • the values obtained in the first step depicted in Figure 4A are correlated to each other.
  • This is depicted in Figure 4B by a matrix.
  • the numbers 1 , 2, 3 and the different geometric forms denote the values of the first to fourth value set of Figure 4A.
  • a "1 " in a diamond standing on the apex denotes a value 1 obtained by the second measuring method 52.
  • a “2" in the semicircular-like form denotes the value 2 from the measurement carried out by the first method 51 .
  • a circle is illustrated at the intersection between the single values in each case, the circle depicting a mathematic connection between the single values.
  • the mathematic connection or relation is a ratio between these values.
  • the values indicated in the line are to be considered as dividend and the values indicated in the column are to be considered as divisor. Consequently, the circles in the matrix of Figure 4B represent the values of the mathematic relation between the single values obtained from the measurements. By these values, the according signature can be generated for characterizing the biological system 100.
  • a buffer solution consists of a sodium phosphate buffer having a concentration of 800 mmol per litre and a pH value of 7.4, 300 ⁇ per litre sodium trimethylsilyl propionate (TSP) and 15 % D 2 0, wherein the nominal pH value was measured prior to D 2 0 addition.
  • Urine and buffer were mixed in an Eppendorf tube and centrifuged for 15 minutes at 4 °C and 17.900 rpm. 500 ⁇ of the supernatant were transferred into a 5-mm NMR sample tube. Subsequently, an NMR measurement took place.
  • NMR spectrometer of the type Advance II+ 600 having a TXI sample head was used for NMR measurement.
  • the height of the sample tube in the spinner was 20 mm and the adjusted actual temperature 298 K.
  • the maximum storage time of the sample at room/measuring temperature was four hours. The measurement was started when a sufficient temperature equilibration was present; this was the case if the temperature fluctuation of the sample was within ⁇ 0.2 K of the nominal temperature.
  • pl9 60 dB TD: 98304 (96k) WDW: EM
  • the NMR spectrum was now deconvoluted, that means transferred into Lorentz and Gauss lines. Subsequently, the spectrum was depicted in vectorized form so that the location of the line (of the signal), the line width, the integral and the configuration are accessible.
  • Table 2 List of twenty peculiar signals.
  • Table 4 Choice of meanin ful matrix elements on the basis of weighted values.
  • This assignment is also depicted in Figure 7 in form of a metabolic network.
  • the effected assignment and the determination of significant correlations of the assigned substances to each other it is possible to determine which of the substances are in interaction to each other within the metabolic pathways.
  • This is an interesting additional aspect which can be co- observed within the framework of the present signature generating.
  • the according signature was compared with comparative signatures of the respective groups. In this manner, it can be determined whether the network of relative substance concentrations, this means the signature, resembles rather the class of kidney rejecters or rather the class of kidney non-rejecters.
  • the signature of the sample to be evaluated was compared in each matrix element with the according matrix element of the signature of the comparative group. Thereby, according differences were calculated and weighted.
  • the similarity of the sample to be evaluated to the respective comparative group finally results as sum over the weighted deviations of all matrix elements. If this measured NMR signature is now compared to a comparative signature of kidney rejecters and a comparative signature of kidney non-rejecters, a distance between the measured NMR signature and the signature of the kidney rejecters as well as the signature of the kidney non-rejecters results from the applied similarity criterion (namely the weighted sum of all deviations between the measurement to be evaluated and the comparative signature).
  • the measured NMR signature 200 can be seen which is located in a distance A from the signature 210 of the group of the kidney non-rejecters and in a distance B from the signature 220 of the group of kidney rejecters.
  • the distance A is smaller than the distance B.
  • the measured NMR signature 200 is to be assigned rather to the signature 210 of the groups of kidney non-rejecters. If the value of the score is higher than 1 , the measured NMR signature 200 is rather to be assigned to the signature 220 of the groups of kidney non-rejecters.
  • Figure 10 a picture comparable to Figure 9 is depicted, wherein here, however, the NMR signatures 200 measured in each case are rather to be assigned to the group of kidney rejecters than to the group of kidney non-rejecters.
  • Figures 9 and 10 thereby serve only for illustration purposes as to how the relation between the measured NMR signatures 200 and the signature 220 of the kidney rejecters and signature 210 of the kidney-non rejecters being already present as reference data sets is to be understood.
  • the calculated score for a measured NMR signature 200 it is possible to determine with which probability a person undergoing kidney transplantation will reject the transplanted kidney.
  • Figure 1 1 a comparison with clinical findings is depicted for validating the present characterization method by a characterizing signature.
  • the scores of the NMR prognosis on the basis of the characterizing signature is depicted.
  • a score of lower than 1 denotes a kidney rejection
  • a score of higher than 1 a kidney non- rejection after transplantation.
  • the kidney rejecters can be correctly predicted to 95 % by the method of NMR signatures. Under these prerequisites, kidney non-rejecters can even be correctly predicted to 100 %.
  • the present method for using a characterizing signature can thus be excellently used for the prediction of the behaviours of a patient on upcoming kidney transplantation.
  • the present method is not limited to such predictions in the area of kidney transplantations, but can find a manifold field of application.
  • Second Exemplary Embodiment Coffee Type Identification A "Nespresso" coffee capsule containing ca. 5 g coffee powder was inserted into a "Nespresso" coffee machine. The coffee powder was extracted by ca. 120 ml hot water according to the standard extraction proceedings performed by the coffee machine. Ca. 1 18 ml liquid coffee extract were obtained. 50 ⁇ sodium phosphate buffer and 25 ⁇ NMR standard (consisting of 2 mmol/l trimethylsilyl propionate [TSP] in D 2 0) were added to 425 ⁇ of the coffee extract. This mixture was then transferred into a 5-mm NMR sample tube for the subsequent NMR measurement.
  • TSP trimethylsilyl propionate
  • NMR spectrometer of the type Advance 11+ 600 having a TXI sample head was used for NMR measurement.
  • the height of the sample tube in the spinner was 20 mm and the adjusted actual temperature was 298 K.
  • the maximum storage time of the sample at room/measuring temperature was four hours. The measurement was started when a sufficient temperature equilibration was present; this was the case if the temperature fluctuation of the sample was within ⁇ 0.2 K of the nominal temperature.
  • pl9 65 dB TD: 32 k WDW: EM
  • Exemplary processed NMR spectra of a caffeine-containing coffee (continuous line) and a decaffeinated coffee (dashed line) are depicted in Figure 12.
  • the intensity on the y-axis is depicted in arbitrary units over the chemical shift in ppm on the x-axis. Signals lying in the range of resonances of water are suppressed. Peaks that could only be observed in the NMR spectrum of caffeine-containing coffee indicate peaks being caused by caffeine. These peaks are marked with an arrow.
  • Figure 13 depicts a measured spectrum (continuous line) and a synthetic spectrum (dashed line).
  • the intensity on the y-axis is depicted in arbitrary units over the chemical shift in ppm on the x-axis.
  • the caffeine content in the decaffeinated coffee types was significantly lower than that of the non-decaffeinated coffee types.
  • "Fortissimo Lungo” contained the highest amount of caffeine
  • “Cosi” was the coffee having the lowest amount in caffeine of the tested non-decaffeinated coffees.
  • Figure 15 schematically depicts the information coarsening.
  • a synthetic spectrum is depicted.
  • a coarsened spectrum consisting only of the best separable lines of this synthetic spectrum are shown.
  • the resulting lines are those lines of the whole spectrum that are as selective as possible.
  • Table 7 Selective peaks used for generating a coffee signature.
  • Table 8 Assignment of causative substances to the selective peaks shown in table 7.
  • Trigonelline (1 -methylpyridinium-3-carboxylate) is present in coffee in an amount of ca. 0.6 %. 50 % of the trigonelline is decomposed by roasting the coffee. Decomposition leads to formation of N-methyl pyridine.
  • Figure 17 shows a difference spectrum between the average spectrum of strongly roasted coffees and the average spectrum of weakly roasted coffees. Substances being present in the group of weakly roasted coffees but not (or to a lower extent) in the group of strongly roasted coffees appear as negative difference peaks. Substances being present in the group of strongly roasted coffees but not (or to a lower extent) in the group of weakly roasted coffees appear as positive difference peaks.
  • quinic acid represents one of the most prominent substances for the total acid content in green coffee.
  • quinic acid progressively increases as the level of chlorogenic acid decreases. It is assumed that formation of quinic acid during roasting results from the cleavage of chlorogenic acid.
  • the content of quinic acid in the coffee is strongly interrelated with the degree of roasting.
  • the peak at 3.554 ppm caused by quinic acid appears to be a sensible choice for generating a characterizing signature for the coffee.
  • a 6x6 correlation matrix was drawn up as described above for each single measured spectrum. This matrix was filled with the values of the quotients between two values (i.e. peak areas) of the analyzed spectrum in each case. Subsequently, meaningful matrix elements were chosen (cf. table 4 and the according description for the general method). These meaningful matrix elements made up the characterizing signature. Based on a first summed matrix for all coffees pre-grouped into the group of strongly roasted coffees and on a second summed matrix for all coffees grouped into the group of weakly roasted coffees, one signature for strongly roasted coffee and one signature for weakly roasted coffee was generated. Subsequently, further coffees were extracted, measured and analyzed in an analogous way.
  • Mustard seeds contain hundreds of compounds. The ratio of these compounds is determined by growth conditions (environmental influence) and the belonging to a certain variety (genetic influence). A small part of a very complex metabolism network is suited to determine the belonging of a mustard seed to a certain variety.
  • the inventors were able to show that 50 features of the complex metabolism network are suited for an according classification (i.e. a clarification of the respective variety).
  • Mustard seeds to be analyzed were chosen by hand to exclude the possibility of contamination of the sample material by small stones, stipes, grains of foreign seed or seeds which are atypically coloured, broken up, germinating or foul.
  • the chosen seeds (ca. 2 g) were ground in a mixer mill for 5 minutes at 30 Hz.
  • the ground stock was transferred after grinding into a sealable polypropylene container and - if not immediately used for measurement - stored at 4 °C for not more than 96 hours.
  • 150 mg ground stock were mixed with 1 .5 ml water and mixed in the mixer mill for 3 minutes at 30 Hz to gain an aqueous extract.
  • 50 ⁇ sodium phosphate buffer and 25 ⁇ NMR standard consisting of 2 mmol/l trimethylsilyl propionate [TSP] in D 2 0) were added to 425 ⁇ of this aqueous extract.
  • This mixture was then transferred into a 5-mm NMR sample tube for the subsequent NMR measurement. The measurement was done under the same conditions as the measurement of the coffee extract according to the second exemplary embodiment.
  • Figure 18 shows an NMR spectrum of mustard seed extract measured in this way.
  • the spectrum consists of ca. 300 relevant peaks.
  • the intensity on the y-axis is depicted in arbitrary units over the chemical shift in ppm on the x-axis. Signals lying in the range of resonances of water are suppressed.
  • the measured NMR spectrum was converted into a synthetic spectrum by line separation (cf. for details the according explanations with respect to the second exemplary embodiment).
  • Figure 19 shows an excerpt of the original spectrum (continuous line) together with its synthetic spectrum (dashed line with broad gaps) and the according integrals of the observed peaks (dashed line with narrow gaps).
  • This correlation matrix can also be referred to as fingerprint matrix.
  • the delta matrix was filled with the value of 1 .0 in all matrix fields. Subsequently, one single value of 1 .0 was raised by 10 % to the value of 1 .1 . If the separation between both groups was ameliorated (i.e. deviation of the considered correlation matrix to the average correlation matrix of the same group became smaller and/or the deviation of the considered correlation matrix to the average correlation matrix of the other group became higher), the value of 1 .1 was used as starting point for the second iteration. If the separation between both groups became worse, the value of 1 .0 was lowered by 10 % to 0.9.
  • the optimized value reached either asymptotically the value being best for the separation or asymptotically zero. If it was lower than a pre-determined threshold value, it was set to zero.
  • the delta matrix contained many fields having the value of 0.0 (insignificant correlations) and some fields having higher values (significant correlations).
  • the delta matrix was simplified to relevant or significant correlations.
  • This weighted delta matrix represents the characterizing signature of the examined system, i.e. of the mustard varieties.
  • the method of generating a signature provides a simplification of numerous relations between individual signals to only significant relations. In other words, all values being present in the signature are weighted with respect to their significance.
  • the thus simplified delta matrix was then multiplied with the tested correlation matrix.
  • a weighted correlation matrix containing many fields having the value of 0.0 (insignificant correlations) and some fields having high values (significant correlations) resulted.
  • a variety-specific characterizing signature was generated according to the preceding explanations for all mustard varieties to be examined.
  • a sample of a non-disclosed mustard variety (blind sample) was analyzed in an analogous way.
  • rating values being indicative for the deviation of the signature of the blind sample from each of the signatures of the already examined mustard varieties were calculated.
  • a weighted correlation matrix of the sample to be tested was compared with mean correlation matrices of already analyzed mustard varieties. The weighting was done by using the according optimized delta matrix (i.e. the signature).
  • a without A thus denotes the sum of the (n - 1 ) mean correlation matrices of all (n - 1 ) mustard varieties being already analyzed with the exception of mustard variety A,.
  • rating values were calculated (namely n rating values), one for each already existing signature or already analyzed mustard variety, respectively.
  • the signature of the blind sample showed the deviations (expressed as rating value) indicated in the following table 9.
  • Table 9 Rating value of the signature of the blind sample in comparison to the signatures of four mustard varieties.
  • the blind sample could be characterized as a sample of the mustard variety "Sinus".
  • the subsequent tables 10 and 1 1 represent further experimental data showing the unambiguous identification of the mustard variety of other blind samples.
  • Table 10 Rating value of the signature of the first further blind sample in comparison to the signatures of eleven mustard varieties.
  • the first further blind sample could be unambiguously assigned to the mustard variety "Tango".
  • Table 1 1 Rating value of the signature of the second further blind sample in comparison to the signatures of eleven mustard varieties.
  • the second further blind sample could be unambiguously assigned to the mustard variety "Sinus”.
  • tumour cells It is known from prior art that the biosynthesis of fatty acids and phospholipids, the biosynthesis of proteins and amino acids and the biosynthesis of nucleotides is activated in tumour cells. Furthermore, an increased amount of reactive oxygen species, an increased occurrence of toxic metabolites (like non-canonical nucleotides) and the activation of unusual and manifold energy sources can be observed in tumour cells.
  • prostate epithelium shows an accumulation of citrate due a limitation of the isocitrate generation.
  • Malignant prostate carcinoma cells loose this capability of citrate accumulation.
  • the citrate concentration in healthy prostate tissue is ca. 20 000 nmol/g, it is only 500 nmol/g (i.e. less than 2.5 %) in malignant tissue.
  • citrate is a central molecule for the overall regulation of metabolism, such a dramatic change in citrate concentration is to be expected to have influence on the biosynthesis of amino acids, proteins and nucleotides.
  • concentration of citrate and intermediates of the citric acid cycle have a direct influence onto the oxidative phosphorylation and lipid metabolism.
  • Creatinine is also a characteristic metabolism product that could be unambiguously identified to play an important role for the tumour-metabolism specific signature. Creatinine is the final product of muscular energy metabolism and is excreted as waste product in the urine. Other identified substances are tightly connected to intermediates of the citric acid cycle. Without going into details, the inventors could assign a relevant metabolic role to almost each of the substances listed in table 12.
  • the composition of the characterizing signature is not arbitrary but indeed reflects the tumour metabolism.
  • Figure 20 represents a network of interrelated substances, the NMR signals of which were considered when generating the characterizing signature.
  • "U1 ", "U2" and “U3” denote three yet unidentified substances. Some exogenic substances like salicylate and tartrate can be most probably attributed to medication. Although they are not directly connected to tumour metabolism, they could reflect secondary effects due to the available metabolic energy for "normal" metabolic processes.
  • NMR spectra of urine obtained on day 85 were analyzed as forth group. These NMR spectra also represented tumour metabolism under androgen suppression.
  • a novel characterizing tumour signature using the NMR spectra of the first group (day 0) and the forth group (day 85) was generated. In doing so, those lines of the NMR spectra of both groups, on the basis of which a statistically significant distinction between both groups was possible, were separated. Then, a weighted correlation matrix (delta matrix) indicating significant correlations between significant lines or peaks was obtained (cf. for the general principle the preceding exemplary embodiments). Instantly, a 61 x61 correlation matrix was drawn up.
  • This second tumour signature showed a high overlap with the first tumour signature (built up on the basis of NMR spectra of the first and second group).
  • E.g., citrate, creatinine, methyl malonate, ⁇ -hydroxy isovalerate, xanthine and hypoxanthine could be identified as substances being causative for the lines considered in this novel signature. This underlines the significance of these substances for monitoring changes in tumour metabolism.
  • the second tumour signature was validated like the first tumour signature, i.e. by comparing it to the signature of men not having a prostate carcinoma.
  • the observed development of the rating values can be used as indication for the progression of anti-tumour therapy: The smaller the deviation between the signature of a tumour patient subjected to therapy and the signature of a person not suffering from a tumour, the better the therapy works.
  • therapy control and assessment can take place.
  • androgen suppressing therapy leads to a significant change in tumour metabolism that can be observed by examining urine samples of patients and analyzing them by NMR techniques using characterizing signatures. It could further be established that therapy progression can be effectively monitored by using characterizing signatures.
  • FIG. 21 a schematic depiction of a computer system is illustrated, on which an exemplary software 300 can be executed which is suited for carrying out a method as explained above.
  • This software 300 is loaded into the memory 310 of a computer 320.
  • the processor 330 now executes the software.
  • experimental data 340 in form of a numeric analysis result of a sample are fed into the memory 310 of the computer.
  • the software 300 now chooses (either automatically or by a user input) a subset of numeric values of the experimental data 340.
  • the processor 330 calculates quotients as mathematic relations between all chosen values, wherein one quotient between two values is calculated at a time.
  • a matrix of n 2 values results, wherein n denotes the number of the chosen values.
  • the significance of the single quotients is determined by the processor 340 while still executing the software 300. Different methods for this purpose have been explained above.
  • This step results in a matrix consisting of many zeros and some values differing from zero.
  • This matrix is the characterizing signature to be generated which is then stored in the memory 310. It is possible to compare this signature with other stored signatures to calculate a distance between the signatures. The result of this comparison can be displayed on a display 350 of the computer 320.
  • This output can, e.g., be information on: a) the probability that a person will reject a transplanted kidney,
  • Figure 22 schematically depicts a flow chart of an embodiment of the method for characterizing a sample explained above.
  • NMR data 400 as analysis result of a sample are provided.
  • a value of a mathematic relation e.g. a quotient
  • many values of the mathematic relation are calculated for individual pairs of two values of the NMR data 400 so that a correlation matrix results in step 410.
  • a characterizing signature is generated on the basis of the matrix obtained in step 410.
  • This signature can be, e.g., be illustrated as delta matrix.
  • Data 400 and processing steps 410 and 420 can be summarized as method 430.
  • FIG 23 schematically depicts a flow chart of an embodiment of the method for characterizing a system explained above.
  • a sample taken from a system to be characterized is taken.
  • This sample is then analyzed in step 510 by an analysis method like NMR spectroscopy.
  • An analysis result is obtained.
  • This analysis result is used to carry out said method 430 for characterizing a sample (cf. Figure 22 for details).
  • a characterizing signature is obtained.
  • the obtained signature is compared to a comparative signature.
  • a deviation of the signature generated in step 430 and the comparative signature is determined. This deviation can, e.g., be a score or a rating value.
  • the deviation is assigned to the system.

Landscapes

  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Medical Informatics (AREA)
  • General Health & Medical Sciences (AREA)
  • High Energy & Nuclear Physics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Theoretical Computer Science (AREA)
  • Evolutionary Biology (AREA)
  • Biotechnology (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Data Mining & Analysis (AREA)
  • General Physics & Mathematics (AREA)
  • Signal Processing (AREA)
  • Software Systems (AREA)
  • Public Health (AREA)
  • Biophysics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Epidemiology (AREA)
  • Artificial Intelligence (AREA)
  • Bioethics (AREA)
  • Databases & Information Systems (AREA)
  • Pathology (AREA)
  • Biochemistry (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Immunology (AREA)
  • Condensed Matter Physics & Semiconductors (AREA)
  • Molecular Biology (AREA)
  • Investigating Or Analysing Biological Materials (AREA)
  • Other Investigation Or Analysis Of Materials By Electrical Means (AREA)
  • Electrotherapy Devices (AREA)

Abstract

La présente invention porte sur un procédé de caractérisation d'un échantillon, comprenant les opérations suivantes : la fourniture d'au moins un résultat d'analyse ayant une pluralité de valeurs, dans lequel le résultat d'analyse a été généré par l'analyse d'un échantillon par au moins un procédé d'analyse, la détermination de la valeur d'au moins une relation mathématique entre au moins deux valeurs de la pluralité de valeurs, la génération d'une signature de caractérisation de l'échantillon sur la base de la valeur de la ou des relations mathématiques. L'invention porte en outre sur un procédé de caractérisation d'un système dans lequel le procédé susmentionné est utilisé.
PCT/EP2011/067383 2010-10-06 2011-10-05 Procédé de caractérisation d'un échantillon par analyse de données Ceased WO2012045773A1 (fr)

Priority Applications (6)

Application Number Priority Date Filing Date Title
AU2011311596A AU2011311596B2 (en) 2010-10-06 2011-10-05 Method for characterizing a sample by data analysis
EP18190951.6A EP3438651B1 (fr) 2010-10-06 2011-10-05 Utilisations de substances de marquage
CA2813863A CA2813863A1 (fr) 2010-10-06 2011-10-05 Procede de caracterisation d'un echantillon par analyse de donnees
US13/878,094 US10497465B2 (en) 2010-10-06 2011-10-05 Method for characterizing a sample by data analysis
EP11778815.8A EP2625511B1 (fr) 2010-10-06 2011-10-05 Procédé de caractérisation d'un échantillon par analyse de données
US16/654,828 US20200051669A1 (en) 2010-10-06 2019-10-16 Method for Characterizing a Sample by Data Analysis

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
DE102010038014.8 2010-10-06
DE102010038014.8A DE102010038014B4 (de) 2010-10-06 2010-10-06 Verwendung spezifischer Substanzen als Marker zur Bestimmung des Risikos einer Nierenabstoßung
US39131710P 2010-10-08 2010-10-08
US61/391,317 2010-10-08

Related Child Applications (2)

Application Number Title Priority Date Filing Date
US13/878,094 A-371-Of-International US10497465B2 (en) 2010-10-06 2011-10-05 Method for characterizing a sample by data analysis
US16/654,828 Continuation US20200051669A1 (en) 2010-10-06 2019-10-16 Method for Characterizing a Sample by Data Analysis

Publications (2)

Publication Number Publication Date
WO2012045773A1 WO2012045773A1 (fr) 2012-04-12
WO2012045773A9 true WO2012045773A9 (fr) 2012-10-18

Family

ID=45872140

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2011/067383 Ceased WO2012045773A1 (fr) 2010-10-06 2011-10-05 Procédé de caractérisation d'un échantillon par analyse de données

Country Status (9)

Country Link
US (2) US10497465B2 (fr)
EP (2) EP3438651B1 (fr)
AU (1) AU2011311596B2 (fr)
CA (1) CA2813863A1 (fr)
DE (1) DE102010038014B4 (fr)
ES (1) ES2963482T3 (fr)
HU (1) HUE064419T2 (fr)
PL (1) PL3438651T3 (fr)
WO (1) WO2012045773A1 (fr)

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018167157A1 (fr) 2017-03-15 2018-09-20 Numares Ag Utilisation d'un ensemble de marqueurs pour déterminer le risque de rejet de rein
US10895618B2 (en) 2016-12-22 2021-01-19 Numares Ag Method for determining proper functioning of an analytic system and control composition for performing said method
WO2025008466A1 (fr) 2023-07-06 2025-01-09 Numares Ag Utilisation d'un biomarqueur pour déterminer le risque d'encéphalopathie hépatique chez un individu
WO2025008465A2 (fr) 2023-07-06 2025-01-09 Numares Ag Utilisation d'un biomarqueur pour déterminer le score de child-pugh selon lequel un individu doit être classé
WO2025008467A2 (fr) 2023-07-06 2025-01-09 Numares Ag Utilisation d'un marqueur ou d'un ensemble de marqueurs pour déterminer le risque qu'un individu présente une fonction hépatique réduite
WO2025008463A1 (fr) 2023-07-06 2025-01-09 Numares Ag Utilisation d'un ensemble de marqueurs pour déterminer le risque d'un individu de présenter un carcinome hépatocellulaire
WO2025008464A2 (fr) 2023-07-06 2025-01-09 Numares Ag Utilisation d'un marqueur ou d'un ensemble de marqueurs pour déterminer le risque pour un individu de souffrir d'ascite
DE102023125871A1 (de) 2023-09-25 2025-03-27 Numares Ag Verwendung eines Markersets zur Bestimmung des Risikos von Blasenkrebs haben oder entwickeln
DE102023125869A1 (de) 2023-09-25 2025-03-27 Numares Ag Verwendung eines Markersets zur Unterscheidung zwischen einem Prostatakarzinom und eine guteartige Prostataveränderung
DE102023125870A1 (de) 2023-09-25 2025-03-27 Numares Ag Verwendung eines Markersets für die Diagnostik des Prostatakarzinoms
DE102023125865A1 (de) 2023-09-25 2025-03-27 Numares Ag Verwendung eines Markersets zur Ermittlung des Risikos der Entwicklung von kastrationsresistentem Prostatakrebs
WO2025068210A2 (fr) 2023-09-25 2025-04-03 Numares Ag Utilisation d'un ensemble de marqueurs pour déterminer l'agressivité d'une tumeur de la prostate

Families Citing this family (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102012205686B3 (de) * 2012-04-05 2013-08-01 Numares Gmbh Verfahren zur Substanzidentifizierung und zugehöriges Computerprogrammprodukt
US9483611B2 (en) 2012-04-27 2016-11-01 Liposcience, Inc. Protective HDL particle number evaluations
US9361429B2 (en) 2012-06-08 2016-06-07 Liposcience, Inc. Multi-parameter diabetes risk evaluations
US9928345B2 (en) 2012-06-08 2018-03-27 Liposciences, Inc. Multiple-marker risk parameters predictive of conversion to diabetes
JP6063367B2 (ja) * 2012-12-04 2017-01-18 本田技研工業株式会社 エポキシ樹脂の混練分散性評価方法
KR101686005B1 (ko) * 2015-02-02 2016-12-13 삼성전자주식회사 자기 공명 영상 장치 및 상기 자기 공명 영상 장치를 이용한 자기 공명 분광법
US11328346B2 (en) 2019-06-24 2022-05-10 International Business Machines Corporation Method, system, and computer program product for product identification using sensory input
CN113176364A (zh) * 2021-04-19 2021-07-27 中国医学科学院北京协和医院 一种同时检测氧化三甲胺和苯乙酰谷氨酰胺的方法及其检测试剂盒和应用
EP4734120A1 (fr) * 2024-10-24 2026-04-29 Roche Diagnostics GmbH Procédé d'identification d'échantillon, système de traitement d'échantillon et support d'informations non transitoire

Family Cites Families (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4689564A (en) 1985-06-10 1987-08-25 General Electric Company Digital interface subsystem for a magnetic resonance imaging and spectroscopy system
DE19727879A1 (de) * 1997-06-30 1999-02-04 Gsf Forschungszentrum Umwelt Verfahren zur Bestimmung der Anzahl von Komponenten in Peaks, Banden und Signalen von Chromatogrammen, Elektrogrammen und Spektrogrammen aller Art
CA2356174A1 (fr) 1998-12-21 2000-06-29 Monash University Detection et traitement de maladies du rein
US6584413B1 (en) 2001-06-01 2003-06-24 Sandia Corporation Apparatus and system for multivariate spectral analysis
WO2003017177A2 (fr) * 2001-08-13 2003-02-27 Beyong Genomics, Inc. Procede et systeme pour l'etablissement de profils de systemes biologiques
US7457708B2 (en) 2003-03-13 2008-11-25 Agilent Technologies Inc Methods and devices for identifying related ions from chromatographic mass spectral datasets containing overlapping components
US20090093010A1 (en) * 2004-09-21 2009-04-09 University Of Manitoba Method of Detecting Kidney Dysfunction
DE102005026133B4 (de) * 2005-06-01 2008-08-07 Gematria Test Lab Gmbh Verfahren zur Bestimmung der antioxidativen Aktivität von Stoffen und Stoffgemischen
DE102009032649B4 (de) * 2009-07-10 2017-12-21 Bruker Daltonik Gmbh Massenspektrometrische Identifizierung von Mikroben nach Unterarten

Cited By (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10895618B2 (en) 2016-12-22 2021-01-19 Numares Ag Method for determining proper functioning of an analytic system and control composition for performing said method
WO2018167157A1 (fr) 2017-03-15 2018-09-20 Numares Ag Utilisation d'un ensemble de marqueurs pour déterminer le risque de rejet de rein
WO2025008466A1 (fr) 2023-07-06 2025-01-09 Numares Ag Utilisation d'un biomarqueur pour déterminer le risque d'encéphalopathie hépatique chez un individu
WO2025008465A2 (fr) 2023-07-06 2025-01-09 Numares Ag Utilisation d'un biomarqueur pour déterminer le score de child-pugh selon lequel un individu doit être classé
WO2025008467A2 (fr) 2023-07-06 2025-01-09 Numares Ag Utilisation d'un marqueur ou d'un ensemble de marqueurs pour déterminer le risque qu'un individu présente une fonction hépatique réduite
WO2025008463A1 (fr) 2023-07-06 2025-01-09 Numares Ag Utilisation d'un ensemble de marqueurs pour déterminer le risque d'un individu de présenter un carcinome hépatocellulaire
WO2025008464A2 (fr) 2023-07-06 2025-01-09 Numares Ag Utilisation d'un marqueur ou d'un ensemble de marqueurs pour déterminer le risque pour un individu de souffrir d'ascite
DE102023125869A1 (de) 2023-09-25 2025-03-27 Numares Ag Verwendung eines Markersets zur Unterscheidung zwischen einem Prostatakarzinom und eine guteartige Prostataveränderung
DE102023125871A1 (de) 2023-09-25 2025-03-27 Numares Ag Verwendung eines Markersets zur Bestimmung des Risikos von Blasenkrebs haben oder entwickeln
DE102023125870A1 (de) 2023-09-25 2025-03-27 Numares Ag Verwendung eines Markersets für die Diagnostik des Prostatakarzinoms
DE102023125865A1 (de) 2023-09-25 2025-03-27 Numares Ag Verwendung eines Markersets zur Ermittlung des Risikos der Entwicklung von kastrationsresistentem Prostatakrebs
WO2025068213A1 (fr) 2023-09-25 2025-04-03 Numares Ag Utilisation d'un ensemble de marqueurs pour déterminer le risque d'avoir ou de développer un cancer de la vessie
WO2025068209A1 (fr) 2023-09-25 2025-04-03 Numares Ag Utilisation d'un ensemble de marqueurs pour déterminer le risque de développer un cancer de la prostate résistant à la castration
WO2025068212A1 (fr) 2023-09-25 2025-04-03 Numares Ag Utilisation d'un ensemble de marqueurs pour le diagnostic d'un carcinome de la prostate
WO2025068211A1 (fr) 2023-09-25 2025-04-03 Numares Ag Utilisation d'un ensemble de marqueurs pour différencier un carcinome de la prostate d'une modification bénigne de la prostate
WO2025068210A2 (fr) 2023-09-25 2025-04-03 Numares Ag Utilisation d'un ensemble de marqueurs pour déterminer l'agressivité d'une tumeur de la prostate
DE102023125866A1 (de) * 2023-09-25 2025-04-10 Numares Ag Verwendung eines Markersets zur Ermittlung der Aggressivität eines Prostatatumors

Also Published As

Publication number Publication date
AU2011311596A1 (en) 2013-05-02
HUE064419T2 (hu) 2024-03-28
EP3438651B1 (fr) 2023-09-13
AU2011311596B2 (en) 2015-12-24
US20130289887A1 (en) 2013-10-31
DE102010038014A1 (de) 2012-04-12
EP2625511B1 (fr) 2018-08-29
CA2813863A1 (fr) 2012-04-12
US10497465B2 (en) 2019-12-03
ES2963482T3 (es) 2024-03-27
EP3438651A2 (fr) 2019-02-06
PL3438651T3 (pl) 2024-02-26
WO2012045773A1 (fr) 2012-04-12
EP2625511A1 (fr) 2013-08-14
DE102010038014B4 (de) 2021-10-07
US20200051669A1 (en) 2020-02-13
EP3438651A3 (fr) 2019-04-17

Similar Documents

Publication Publication Date Title
US20200051669A1 (en) Method for Characterizing a Sample by Data Analysis
Santos et al. High-resolution magic angle spinning nuclear magnetic resonance in foodstuff analysis
Charlton et al. Application of 1H NMR and multivariate statistics for screening complex mixtures: quality control and authenticity of instant coffee
Weljie et al. Targeted profiling: quantitative analysis of 1H NMR metabolomics data
Fonville et al. Evaluation of full-resolution J-resolved 1H NMR projections of biofluids for metabonomics information retrieval and biomarker identification
Santone et al. Saliva metabolomics by NMR for the evaluation of sport performance
Maxwell et al. Pattern recognition analysis of 1H NMR spectra from perchloric acid extracts of human brain tumor biopsies
CN1201148C (zh) 药用植物制品的品质管制及标准化方法
Mazzei et al. Assessment of geographical origin and production period of royal jelly by NMR metabolomics
CN106680307A (zh) 一种基于核磁共振技术判别大豆种子活力的方法
Mazzei et al. HRMAS-NMR metabolomics of Aglianicone grapes pulp to evaluate terroir and vintage effects, and, as assessed by the electromagnetic induction (EMI) technique, spatial variability of vineyard soils
CN102305802B (zh) Hbv感染性与酒精性肝硬化差异表达图谱模型及其构建方法
CA2864026C (fr) Diagnostic de la sclerose en plaques
HK40004176A (en) Uses of marker substances
HK40004176B (en) Uses of marker substances
Kaiser et al. Metabolic profiling
CN111638294A (zh) 一种小叶金花草的鉴别方法及其应用
Noh et al. Application of mass spectrometer-based electronic nose for discrimination of Angelicae gigantis radix
CN120613144B (zh) 一种基于血液nmr编辑谱的药物敏感度预测方法
Zhong et al. Analysis of the quality of Gastrodin elata Blume with different processing methods using a combination of NIR, FTIR and chemometrics
Mi Park et al. Inter-and intra-individual variations of urinary endogenous metabolites in healthy male college students using 1 H NMR spectroscopy.
Park et al. Inter-and intra-individual variations of urinary endogenous metabolites in healthy male college students using 1H NMR spectroscopy
Singh et al. Enhancing detection of low‑abundance metabolites in proton NMR through band‑selective suppression and presaturation
Colafranceschi et al. Dissecting drug and vehicle metabolic effects in rats by a metabonomic approach
Wine et al. R. Consonni and LR Cagliani

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 11778815

Country of ref document: EP

Kind code of ref document: A1

WWE Wipo information: entry into national phase

Ref document number: 2011778815

Country of ref document: EP

ENP Entry into the national phase

Ref document number: 2813863

Country of ref document: CA

WWE Wipo information: entry into national phase

Ref document number: 13878094

Country of ref document: US

NENP Non-entry into the national phase

Ref country code: DE

ENP Entry into the national phase

Ref document number: 2011311596

Country of ref document: AU

Date of ref document: 20111005

Kind code of ref document: A